@conference {13217,
	title = {Window-based, discontinuity preserving stereo},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {1},
	year = {2004},
	month = {2004/07/02/june},
	pages = {I-66 - I-73 Vol.1 - I-66 - I-73 Vol.1},
	abstract = {Traditionally, the problem of stereo matching has been addressed either by a local window-based approach or a dense pixel-based approach using global optimization. In this paper we present an algorithm which combines window-based local matching into a global optimization framework. Our local matching algorithm assumes that local windows can have at most two disparities. Under this assumption, the local matching can be performed very efficiently using graph cuts. The global matching is formulated as minimization of an energy term that takes into account the matching constraints induced by the local stereo algorithm. Fast, approximate minimization of this energy is achieved through graph cuts. The key feature of our algorithm is that it preserves discontinuities both during the local as well as global matching phase.},
	keywords = {algorithm;, approach;, based, cuts;, dense, discontinuity, global, graph, image, local, MATCHING, matching;, minimisation;, optimization;, Pixel, preserving, processing;, stereo, theory;, window},
	doi = {10.1109/CVPR.2004.1315015},
	author = {Agrawal,M. and Davis, Larry S.}
}
@article {12719,
	title = {Accurate dense optical flow estimation using adaptive structure tensors and a parametric model},
	journal = {Image Processing, IEEE Transactions on},
	volume = {12},
	year = {2003},
	month = {2003/10//},
	pages = {1170 - 1180},
	abstract = {An accurate optical flow estimation algorithm is proposed in this paper. By combining the three-dimensional (3D) structure tensor with a parametric flow model, the optical flow estimation problem is converted to a generalized eigenvalue problem. The optical flow can be accurately estimated from the generalized eigenvectors. The confidence measure derived from the generalized eigenvalues is used to adaptively adjust the coherent motion region to further improve the accuracy. Experiments using both synthetic sequences with ground truth and real sequences illustrate our method. Comparisons with classical and recently published methods are also given to demonstrate the accuracy of our algorithm.},
	keywords = {3D, accuracy;, adaptive, and, coherent, confidence, dense, eigenfunctions;, eigenvalue, eigenvalues, eigenvectors;, estimation;, flow, generalized, ground, image, measure;, model;, MOTION, optical, parameter, parametric, problem;, real, region;, sequences;, structure, synthetic, tensor;, tensors;, three-dimensional, truth;},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2003.815296},
	author = {Liu,Haiying and Chellapa, Rama and Rosenfeld, A.}
}
